Human tactile sensing and sensorimotor mechanism: from afferent tactile signals to efferent motor control

In tactile sensing, decoding the journey from afferent tactile signals to efferent motor commands is a significant challenge primarily due to the difficulty in capturing population-level afferent nerve signals during active touch. This study integrates a finite element hand model with a neural dynamic model by using microneurography data to predict neural responses based on contact biomechanics and membrane transduction dynamics. This research focuses specifically on tactile sensation and its direct translation into motor actions. Evaluations of muscle synergy during in -vivo experiments revealed transduction functions linking tactile signals and muscle activation. These functions suggest similar sensorimotor strategies for grasping influenced by object size and weight. The decoded transduction mechanism was validated by restoring human-like sensorimotor performance on a tendon-driven biomimetic hand. This research advances our understanding of translating tactile sensation into motor actions, offering valuable insights into prosthetic design, robotics, and the development of next-generation prosthetics with neuromorphic tactile feedback.

Sensitivity Analysis: We conducted sensitivity analyses to understand the influence of various material properties and loading conditions on the model's predictions.

Implementation in Current Research:
In our study, the FE hand model is used to simulate the mechanical responses of the hand during active touch scenarios.The model's outputs, such as strain and stress distributions, are crucial inputs for our multi-level numerical model that predicts afferent neural signals.
This FE model of the human hand, with its detailed anatomical structure and validated biomechanical properties, plays a pivotal role in our research.It allows us to bridge the gap between biomechanical interactions and neural processing, enhancing our understanding of tactile perception.

Determination:
Obtaining Laplace Coefficients: • Methodology: The Laplace coefficients were determined using a combination of system identification techniques and machine learning algorithms, utilizing extensive datasets from both simulated and experimental neural data.• Process: These coefficients were optimized through a least square fitting procedure, iteratively adjusted to minimize the error between model predictions and observed data, enhancing model accuracy and stability.
Tuning Active/Reactive Parameters: • Active Parameters: Tuned based on proactive interactions required in active grasping scenarios, using trial-and-error in controlled experimental settings to align closely with human performance metrics.• Reactive Parameters: Adjusted for quicker responsiveness and higher sensitivity in reactive scenarios, involving dynamic simulations to handle sudden changes effectively.

Determination of Transduction Functions:
• Mathematical Formulation: We selected specific forms of transduction         For the in-vivo discrimination test, the subject was blindfolded and asked to sit at a table.
Cylinders or spheres were presented in pairs, either with the same or different diameters.The subject was required to judge whether the pairs of cylinders or spheres were the same or not.Only the index finger was allowed to touch the objects, and the subject's wrist was fixed.The test was conducted in blocks, with each block containing 20 comparisons (10 pairs of 50mm-50mm objects and 10 pairs of 50mm-60mm or other diameters, spheres or cylinders with all five different diameters presented in each session).The pairs of surfaces varied randomly from block to block.In total, 10 blocks were performed (5 for cylinders and 5 for spheres).The probability of detection was calculated for each cylinder or sphere, and the entire test was repeated 3 times to ensure reliability and generality of the results.Before the test, several practice blocks were conducted to train the subjects and ensure the reliability of the experimental results.

Fig. S2 .
Fig. S2.In-vivo grasping experimental results based on all six human subjects

Fig. S4 .
Fig. S4.The neural activation levels predicted based on summarized transduction function compared with those computed based on the electromyography signals captured from the human subject.

Fig. S10 .
Fig. S10.The measured neural activation levels and those predicted based on summarized transduction function.

Fig. S12 .
Fig. S12.The hardware setting of the artificial tactile sensory system.

Fig. S13 .
Fig. S13.The computing of discrimination accuracy base on the neural features of spiking rate and Victor-Purpura distance.

Fig. S14 .
Fig. S14.The artificial tactile sensory system mounted on the Kuka robotic arm.

Table .
S11-20.The transduction functions extracted from the neural activation levels of the other five human subjects.Table.S21.The gender and age of all the human subjects.

Table S22 . Victor-Purpura Distances Between Baseline Cylinder/Spherical Object (Diameter: 100mm) and Others.
Each object underwent 10 touches, and Victor-Purpura distances were computed between cylinders/spheres with varying diameters (ranging from 50mm to 90mm) and the baseline diameter of 100mm.Additionally, Victor-Purpura distances among the 10 trials of touching the baseline cylinders/sphere were calculated and utilized as 'noise' for signal detection theory to calculate the hit rate.This table presents the average Victor-Purpura distances along with their standard deviations.